Overview

Dataset statistics

Number of variables26
Number of observations14606
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory208.0 B

Variable types

Text1
Categorical3
Numeric17
DateTime4
Boolean1

Alerts

cons_12m is highly overall correlated with cons_last_month and 2 other fieldsHigh correlation
cons_gas_12m is highly overall correlated with nb_prod_actHigh correlation
cons_last_month is highly overall correlated with cons_12m and 2 other fieldsHigh correlation
forecast_cons_12m is highly overall correlated with cons_12m and 3 other fieldsHigh correlation
forecast_cons_year is highly overall correlated with cons_last_month and 3 other fieldsHigh correlation
forecast_meter_rent_12m is highly overall correlated with forecast_price_energy_off_peak and 3 other fieldsHigh correlation
forecast_price_energy_off_peak is highly overall correlated with forecast_meter_rent_12m and 2 other fieldsHigh correlation
forecast_price_energy_peak is highly overall correlated with forecast_meter_rent_12m and 2 other fieldsHigh correlation
forecast_price_pow_off_peak is highly overall correlated with forecast_meter_rent_12m and 3 other fieldsHigh correlation
imp_cons is highly overall correlated with cons_last_month and 3 other fieldsHigh correlation
margin_gross_pow_ele is highly overall correlated with margin_net_pow_eleHigh correlation
margin_net_pow_ele is highly overall correlated with margin_gross_pow_eleHigh correlation
nb_prod_act is highly overall correlated with cons_gas_12mHigh correlation
net_margin is highly overall correlated with cons_12m and 3 other fieldsHigh correlation
pow_max is highly overall correlated with forecast_meter_rent_12m and 3 other fieldsHigh correlation
churn is highly imbalanced (54.0%)Imbalance
net_margin is highly skewed (γ1 = 36.56951466)Skewed
id has unique valuesUnique
cons_gas_12m has 11994 (82.1%) zerosZeros
cons_last_month has 4983 (34.1%) zerosZeros
forecast_cons_12m has 306 (2.1%) zerosZeros
forecast_cons_year has 6148 (42.1%) zerosZeros
forecast_discount_energy has 14094 (96.5%) zerosZeros
forecast_meter_rent_12m has 725 (5.0%) zerosZeros
forecast_price_energy_peak has 7021 (48.1%) zerosZeros
imp_cons has 6169 (42.2%) zerosZeros
margin_gross_pow_ele has 157 (1.1%) zerosZeros
margin_net_pow_ele has 157 (1.1%) zerosZeros
net_margin has 185 (1.3%) zerosZeros

Reproduction

Analysis started2023-08-14 19:38:26.076999
Analysis finished2023-08-14 19:38:51.000673
Duration24.92 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct14606
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:51.361859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters467392
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14606 ?
Unique (%)100.0%

Sample

1st row24011ae4ebbe3035111d65fa7c15bc57
2nd rowd29c2c54acc38ff3c0614d0a653813dd
3rd row764c75f661154dac3a6c254cd082ea7d
4th rowbba03439a292a1e166f80264c16191cb
5th row149d57cf92fc41cf94415803a877cb4b
ValueCountFrequency (%)
24011ae4ebbe3035111d65fa7c15bc57 1
 
< 0.1%
26c7bba7d51f86a16109de505bcd4f52 1
 
< 0.1%
74ff037708f036de5745ce34d8d9d4df 1
 
< 0.1%
21860c2ff2d5df75503b230ce629c253 1
 
< 0.1%
764c75f661154dac3a6c254cd082ea7d 1
 
< 0.1%
bba03439a292a1e166f80264c16191cb 1
 
< 0.1%
149d57cf92fc41cf94415803a877cb4b 1
 
< 0.1%
1aa498825382410b098937d65c4ec26d 1
 
< 0.1%
7ab4bf4878d8f7661dfc20e9b8e18011 1
 
< 0.1%
01495c955be7ec5e7f3203406785aae0 1
 
< 0.1%
Other values (14596) 14596
99.9%
2023-08-14T14:38:51.772536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 29453
 
6.3%
e 29432
 
6.3%
7 29400
 
6.3%
4 29381
 
6.3%
2 29375
 
6.3%
b 29313
 
6.3%
3 29297
 
6.3%
1 29257
 
6.3%
c 29210
 
6.2%
a 29169
 
6.2%
Other values (6) 174105
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 292239
62.5%
Lowercase Letter 175153
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 29453
10.1%
7 29400
10.1%
4 29381
10.1%
2 29375
10.1%
3 29297
10.0%
1 29257
10.0%
8 29153
10.0%
0 29090
10.0%
9 29012
9.9%
5 28821
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 29432
16.8%
b 29313
16.7%
c 29210
16.7%
a 29169
16.7%
d 29137
16.6%
f 28892
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 292239
62.5%
Latin 175153
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 29453
10.1%
7 29400
10.1%
4 29381
10.1%
2 29375
10.1%
3 29297
10.0%
1 29257
10.0%
8 29153
10.0%
0 29090
10.0%
9 29012
9.9%
5 28821
9.9%
Latin
ValueCountFrequency (%)
e 29432
16.8%
b 29313
16.7%
c 29210
16.7%
a 29169
16.7%
d 29137
16.6%
f 28892
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 467392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 29453
 
6.3%
e 29432
 
6.3%
7 29400
 
6.3%
4 29381
 
6.3%
2 29375
 
6.3%
b 29313
 
6.3%
3 29297
 
6.3%
1 29257
 
6.3%
c 29210
 
6.2%
a 29169
 
6.2%
Other values (6) 174105
37.3%

channel_sales
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
foosdfpfkusacimwkcsosbicdxkicaua
6754 
MISSING
3725 
lmkebamcaaclubfxadlmueccxoimlema
1843 
usilxuppasemubllopkaafesmlibmsdf
1375 
ewpakwlliwisiwduibdlfmalxowmwpci
893 
Other values (3)
 
16

Length

Max length32
Median length32
Mean length25.624196
Min length7

Characters and Unicode

Total characters374267
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfoosdfpfkusacimwkcsosbicdxkicaua
2nd rowMISSING
3rd rowfoosdfpfkusacimwkcsosbicdxkicaua
4th rowlmkebamcaaclubfxadlmueccxoimlema
5th rowMISSING

Common Values

ValueCountFrequency (%)
foosdfpfkusacimwkcsosbicdxkicaua 6754
46.2%
MISSING 3725
25.5%
lmkebamcaaclubfxadlmueccxoimlema 1843
 
12.6%
usilxuppasemubllopkaafesmlibmsdf 1375
 
9.4%
ewpakwlliwisiwduibdlfmalxowmwpci 893
 
6.1%
sddiedcslfslkckwlfkdpoeeailfpeds 11
 
0.1%
epumfxlbckeskwekxbiuasklxalciiuu 3
 
< 0.1%
fixdbufsefwooaasfcxdxadsiekoceaa 2
 
< 0.1%

Length

2023-08-14T14:38:51.857029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T14:38:51.938780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
foosdfpfkusacimwkcsosbicdxkicaua 6754
46.2%
missing 3725
25.5%
lmkebamcaaclubfxadlmueccxoimlema 1843
 
12.6%
usilxuppasemubllopkaafesmlibmsdf 1375
 
9.4%
ewpakwlliwisiwduibdlfmalxowmwpci 893
 
6.1%
sddiedcslfslkckwlfkdpoeeailfpeds 11
 
0.1%
epumfxlbckeskwekxbiuasklxalciiuu 3
 
< 0.1%
fixdbufsefwooaasfcxdxadsiekoceaa 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 35415
 
9.5%
c 35313
 
9.4%
s 33465
 
8.9%
i 29355
 
7.8%
f 25792
 
6.9%
k 24420
 
6.5%
o 24390
 
6.5%
u 22226
 
5.9%
m 21883
 
5.8%
d 18573
 
5.0%
Other values (11) 103435
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 348192
93.0%
Uppercase Letter 26075
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35415
10.2%
c 35313
10.1%
s 33465
9.6%
i 29355
 
8.4%
f 25792
 
7.4%
k 24420
 
7.0%
o 24390
 
7.0%
u 22226
 
6.4%
m 21883
 
6.3%
d 18573
 
5.3%
Other values (6) 77360
22.2%
Uppercase Letter
ValueCountFrequency (%)
I 7450
28.6%
S 7450
28.6%
M 3725
14.3%
N 3725
14.3%
G 3725
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 374267
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35415
 
9.5%
c 35313
 
9.4%
s 33465
 
8.9%
i 29355
 
7.8%
f 25792
 
6.9%
k 24420
 
6.5%
o 24390
 
6.5%
u 22226
 
5.9%
m 21883
 
5.8%
d 18573
 
5.0%
Other values (11) 103435
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 35415
 
9.5%
c 35313
 
9.4%
s 33465
 
8.9%
i 29355
 
7.8%
f 25792
 
6.9%
k 24420
 
6.5%
o 24390
 
6.5%
u 22226
 
5.9%
m 21883
 
5.8%
d 18573
 
5.0%
Other values (11) 103435
27.6%

cons_12m
Real number (ℝ)

HIGH CORRELATION 

Distinct11065
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159220.29
Minimum0
Maximum6207104
Zeros117
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:52.030200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1512.25
Q15674.75
median14115.5
Q340763.75
95-th percentile913771.75
Maximum6207104
Range6207104
Interquartile range (IQR)35089

Descriptive statistics

Standard deviation573465.26
Coefficient of variation (CV)3.6017098
Kurtosis42.689777
Mean159220.29
Median Absolute Deviation (MAD)10669
Skewness5.9973081
Sum2.3255715 × 109
Variance3.2886241 × 1011
MonotonicityNot monotonic
2023-08-14T14:38:52.110319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117
 
0.8%
2882597 27
 
0.2%
3329244 24
 
0.2%
1743025 18
 
0.1%
3926060 18
 
0.1%
6207104 18
 
0.1%
1722179 17
 
0.1%
2288838 17
 
0.1%
2503923 16
 
0.1%
963288 16
 
0.1%
Other values (11055) 14318
98.0%
ValueCountFrequency (%)
0 117
0.8%
1 2
 
< 0.1%
2 2
 
< 0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
9 2
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
6207104 18
0.1%
5731448 14
0.1%
5322441 1
 
< 0.1%
5161456 4
 
< 0.1%
4939487 4
 
< 0.1%
4406520 14
0.1%
4306656 9
0.1%
4199490 5
 
< 0.1%
4100379 13
0.1%
4012564 2
 
< 0.1%

cons_gas_12m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2112
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28092.375
Minimum0
Maximum4154590
Zeros11994
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:52.191936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile75854
Maximum4154590
Range4154590
Interquartile range (IQR)0

Descriptive statistics

Standard deviation162973.06
Coefficient of variation (CV)5.8013271
Kurtosis126.33363
Mean28092.375
Median Absolute Deviation (MAD)0
Skewness9.59753
Sum4.1031723 × 108
Variance2.6560218 × 1010
MonotonicityNot monotonic
2023-08-14T14:38:52.266277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11994
82.1%
976731 27
 
0.2%
867921 24
 
0.2%
41532 18
 
0.1%
1959386 18
 
0.1%
1192414 17
 
0.1%
475413 16
 
0.1%
468369 15
 
0.1%
1337056 14
 
0.1%
187578 13
 
0.1%
Other values (2102) 2450
 
16.8%
ValueCountFrequency (%)
0 11994
82.1%
11 7
 
< 0.1%
12 2
 
< 0.1%
21 2
 
< 0.1%
32 1
 
< 0.1%
35 2
 
< 0.1%
36 1
 
< 0.1%
41 1
 
< 0.1%
43 2
 
< 0.1%
46 1
 
< 0.1%
ValueCountFrequency (%)
4154590 2
 
< 0.1%
2813019 2
 
< 0.1%
2055098 2
 
< 0.1%
1959386 18
0.1%
1860052 4
 
< 0.1%
1859491 3
 
< 0.1%
1813943 1
 
< 0.1%
1711930 1
 
< 0.1%
1653924 2
 
< 0.1%
1542867 1
 
< 0.1%

cons_last_month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4751
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16090.27
Minimum0
Maximum771203
Zeros4983
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:52.346963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median792.5
Q33383
95-th percentile82161.5
Maximum771203
Range771203
Interquartile range (IQR)3383

Descriptive statistics

Standard deviation64364.196
Coefficient of variation (CV)4.0001937
Kurtosis47.762991
Mean16090.27
Median Absolute Deviation (MAD)792.5
Skewness6.391407
Sum2.3501448 × 108
Variance4.1427498 × 109
MonotonicityNot monotonic
2023-08-14T14:38:52.428591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4983
34.1%
382647 27
 
0.2%
509826 24
 
0.2%
558120 18
 
0.1%
469210 18
 
0.1%
106161 18
 
0.1%
181187 17
 
0.1%
237044 17
 
0.1%
54281 16
 
0.1%
313018 16
 
0.1%
Other values (4741) 9452
64.7%
ValueCountFrequency (%)
0 4983
34.1%
1 13
 
0.1%
2 5
 
< 0.1%
3 5
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
771203 14
0.1%
760727 1
 
< 0.1%
612247 1
 
< 0.1%
558120 18
0.1%
509826 24
0.2%
507598 14
0.1%
479030 9
 
0.1%
469898 2
 
< 0.1%
469210 18
0.1%
456462 5
 
< 0.1%
Distinct1796
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2003-05-09 00:00:00
Maximum2014-09-01 00:00:00
2023-08-14T14:38:52.516403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:52.947370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct368
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2016-01-28 00:00:00
Maximum2017-06-13 00:00:00
2023-08-14T14:38:53.030862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:53.106303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2129
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2003-05-09 00:00:00
Maximum2016-01-29 00:00:00
2023-08-14T14:38:53.181513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:53.258723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct386
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2013-06-26 00:00:00
Maximum2016-01-28 00:00:00
2023-08-14T14:38:53.343398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:53.423507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

forecast_cons_12m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13993
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1868.6149
Minimum0
Maximum82902.83
Zeros306
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:53.507722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84.9175
Q1494.995
median1112.875
Q32401.79
95-th percentile6127.095
Maximum82902.83
Range82902.83
Interquartile range (IQR)1906.795

Descriptive statistics

Standard deviation2387.5715
Coefficient of variation (CV)1.2777226
Kurtosis147.42668
Mean1868.6149
Median Absolute Deviation (MAD)752.955
Skewness7.1558526
Sum27292989
Variance5700497.8
MonotonicityNot monotonic
2023-08-14T14:38:53.582072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 306
 
2.1%
0.15 6
 
< 0.1%
415.14 4
 
< 0.1%
0.45 3
 
< 0.1%
1539.37 3
 
< 0.1%
651.21 3
 
< 0.1%
442.74 3
 
< 0.1%
335.5 3
 
< 0.1%
0.3 3
 
< 0.1%
303.93 3
 
< 0.1%
Other values (13983) 14269
97.7%
ValueCountFrequency (%)
0 306
2.1%
0.1 1
 
< 0.1%
0.15 6
 
< 0.1%
0.18 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3 3
 
< 0.1%
0.32 1
 
< 0.1%
0.33 1
 
< 0.1%
0.42 2
 
< 0.1%
0.45 3
 
< 0.1%
ValueCountFrequency (%)
82902.83 1
< 0.1%
61357.17 1
< 0.1%
48412.58 1
< 0.1%
35789.29 1
< 0.1%
35312.21 1
< 0.1%
32174.47 1
< 0.1%
31347.11 1
< 0.1%
30533.99 1
< 0.1%
28375.76 1
< 0.1%
27618.39 1
< 0.1%

forecast_cons_year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4218
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1399.7629
Minimum0
Maximum175375
Zeros6148
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:53.653901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median314
Q31745.75
95-th percentile5968.75
Maximum175375
Range175375
Interquartile range (IQR)1745.75

Descriptive statistics

Standard deviation3247.7863
Coefficient of variation (CV)2.3202403
Kurtosis653.73441
Mean1399.7629
Median Absolute Deviation (MAD)314
Skewness16.58799
Sum20444937
Variance10548116
MonotonicityNot monotonic
2023-08-14T14:38:53.730147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6148
42.1%
1 13
 
0.1%
8 13
 
0.1%
7 11
 
0.1%
453 11
 
0.1%
2 9
 
0.1%
524 9
 
0.1%
310 9
 
0.1%
420 9
 
0.1%
173 9
 
0.1%
Other values (4208) 8365
57.3%
ValueCountFrequency (%)
0 6148
42.1%
1 13
 
0.1%
2 9
 
0.1%
3 5
 
< 0.1%
4 8
 
0.1%
5 7
 
< 0.1%
6 5
 
< 0.1%
7 11
 
0.1%
8 13
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
175375 1
< 0.1%
79127 1
< 0.1%
70180 1
< 0.1%
66643 1
< 0.1%
63969 1
< 0.1%
59460 1
< 0.1%
51604 1
< 0.1%
51336 1
< 0.1%
50106 1
< 0.1%
46491 1
< 0.1%

forecast_discount_energy
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.966726
Minimum0
Maximum30
Zeros14094
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:53.797279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.1082887
Coefficient of variation (CV)5.2841122
Kurtosis24.854712
Mean0.966726
Median Absolute Deviation (MAD)0
Skewness5.1550983
Sum14120
Variance26.094613
MonotonicityNot monotonic
2023-08-14T14:38:53.848758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 14094
96.5%
30 260
 
1.8%
28 102
 
0.7%
24 83
 
0.6%
22 47
 
0.3%
25 7
 
< 0.1%
26 5
 
< 0.1%
19 2
 
< 0.1%
17 2
 
< 0.1%
23 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 14094
96.5%
5 1
 
< 0.1%
10 1
 
< 0.1%
17 2
 
< 0.1%
19 2
 
< 0.1%
22 47
 
0.3%
23 2
 
< 0.1%
24 83
 
0.6%
25 7
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
30 260
1.8%
28 102
 
0.7%
26 5
 
< 0.1%
25 7
 
< 0.1%
24 83
 
0.6%
23 2
 
< 0.1%
22 47
 
0.3%
19 2
 
< 0.1%
17 2
 
< 0.1%
10 1
 
< 0.1%

forecast_meter_rent_12m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3528
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.086871
Minimum0
Maximum599.31
Zeros725
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:53.916286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3325
Q116.18
median18.795
Q3131.03
95-th percentile145.72
Maximum599.31
Range599.31
Interquartile range (IQR)114.85

Descriptive statistics

Standard deviation66.165783
Coefficient of variation (CV)1.0488043
Kurtosis4.4915214
Mean63.086871
Median Absolute Deviation (MAD)9.315
Skewness1.5051479
Sum921446.84
Variance4377.9108
MonotonicityNot monotonic
2023-08-14T14:38:53.990805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 725
 
5.0%
131.76 238
 
1.6%
18.32 162
 
1.1%
18.37 131
 
0.9%
129.61 103
 
0.7%
15.98 100
 
0.7%
131.38 78
 
0.5%
18.47 77
 
0.5%
16.26 76
 
0.5%
18.42 74
 
0.5%
Other values (3518) 12842
87.9%
ValueCountFrequency (%)
0 725
5.0%
0.09 1
 
< 0.1%
0.18 1
 
< 0.1%
0.24 1
 
< 0.1%
0.27 1
 
< 0.1%
0.31 1
 
< 0.1%
0.33 1
 
< 0.1%
0.34 2
 
< 0.1%
0.35 2
 
< 0.1%
0.36 6
 
< 0.1%
ValueCountFrequency (%)
599.31 5
< 0.1%
585.62 1
 
< 0.1%
562.13 1
 
< 0.1%
552.9 1
 
< 0.1%
548.41 1
 
< 0.1%
439.67 1
 
< 0.1%
434.04 1
 
< 0.1%
407.98 1
 
< 0.1%
407.97 9
0.1%
406.45 1
 
< 0.1%

forecast_price_energy_off_peak
Real number (ℝ)

HIGH CORRELATION 

Distinct516
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13728327
Minimum0
Maximum0.273963
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:54.068583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11293
Q10.11634
median0.143166
Q30.146348
95-th percentile0.166178
Maximum0.273963
Range0.273963
Interquartile range (IQR)0.030008

Descriptive statistics

Standard deviation0.024622862
Coefficient of variation (CV)0.17935808
Kurtosis8.3645386
Mean0.13728327
Median Absolute Deviation (MAD)0.019738
Skewness-0.11958602
Sum2005.1594
Variance0.00060628535
MonotonicityNot monotonic
2023-08-14T14:38:54.141565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.145711 933
 
6.4%
0.144902 732
 
5.0%
0.115174 726
 
5.0%
0.146694 644
 
4.4%
0.115237 595
 
4.1%
0.11691 449
 
3.1%
0.1169 406
 
2.8%
0.146348 339
 
2.3%
0.143166 335
 
2.3%
0.116509 308
 
2.1%
Other values (506) 9139
62.6%
ValueCountFrequency (%)
0 22
 
0.2%
0.0006 66
0.5%
0.000901 6
 
< 0.1%
0.092453 73
0.5%
0.094486 1
 
< 0.1%
0.095022 2
 
< 0.1%
0.095061 1
 
< 0.1%
0.095558 1
 
< 0.1%
0.095919 1
 
< 0.1%
0.096095 1
 
< 0.1%
ValueCountFrequency (%)
0.273963 18
0.1%
0.273957 2
 
< 0.1%
0.272981 21
0.1%
0.272972 1
 
< 0.1%
0.245926 8
 
0.1%
0.245347 6
 
< 0.1%
0.237776 11
 
0.1%
0.236794 4
 
< 0.1%
0.236291 3
 
< 0.1%
0.229272 34
0.2%

forecast_price_energy_peak
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct329
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050490767
Minimum0
Maximum0.195975
Zeros7021
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:54.224635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.084138
Q30.098837
95-th percentile0.10175
Maximum0.195975
Range0.195975
Interquartile range (IQR)0.098837

Descriptive statistics

Standard deviation0.049036507
Coefficient of variation (CV)0.97119751
Kurtosis-1.8907547
Mean0.050490767
Median Absolute Deviation (MAD)0.0311955
Skewness-0.014331428
Sum737.46815
Variance0.002404579
MonotonicityNot monotonic
2023-08-14T14:38:54.302118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7021
48.1%
0.098837 722
 
4.9%
0.100123 596
 
4.1%
0.100015 473
 
3.2%
0.100572 445
 
3.0%
0.101397 308
 
2.1%
0.103487 288
 
2.0%
0.087381 169
 
1.2%
0.086803 159
 
1.1%
0.099419 153
 
1.0%
Other values (319) 4272
29.2%
ValueCountFrequency (%)
0 7021
48.1%
0.076592 1
 
< 0.1%
0.077124 1
 
< 0.1%
0.078125 1
 
< 0.1%
0.078641 2
 
< 0.1%
0.078859 1
 
< 0.1%
0.079221 2
 
< 0.1%
0.079281 2
 
< 0.1%
0.079771 1
 
< 0.1%
0.079799 1
 
< 0.1%
ValueCountFrequency (%)
0.195975 1
 
< 0.1%
0.168092 6
 
< 0.1%
0.168032 7
 
< 0.1%
0.146676 8
 
0.1%
0.136336 59
0.4%
0.13608 1
 
< 0.1%
0.135761 5
 
< 0.1%
0.135732 35
0.2%
0.135182 3
 
< 0.1%
0.134604 2
 
< 0.1%

forecast_price_pow_off_peak
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.130056
Minimum0
Maximum59.266378
Zeros94
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:54.379672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.606701
Q140.606701
median44.311378
Q344.311378
95-th percentile46.305378
Maximum59.266378
Range59.266378
Interquartile range (IQR)3.704677

Descriptive statistics

Standard deviation4.4859882
Coefficient of variation (CV)0.10401072
Kurtosis54.708041
Mean43.130056
Median Absolute Deviation (MAD)0.9969996
Skewness-4.998772
Sum629957.59
Variance20.12409
MonotonicityNot monotonic
2023-08-14T14:38:54.447493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
44.31137796 6933
47.5%
40.606701 4651
31.8%
45.80687796 697
 
4.8%
46.30537836 615
 
4.2%
45.30837756 419
 
2.9%
41.1052014 302
 
2.1%
40.9390266 237
 
1.6%
41.2713642 160
 
1.1%
58.99595196 118
 
0.8%
0 94
 
0.6%
Other values (31) 380
 
2.6%
ValueCountFrequency (%)
0 94
 
0.6%
35.55576792 1
 
< 0.1%
37.929294 2
 
< 0.1%
40.606701 4651
31.8%
40.728885 31
 
0.2%
40.9390266 237
 
1.6%
41.1052014 302
 
2.1%
41.1067014 1
 
< 0.1%
41.2713642 160
 
1.1%
41.2718682 1
 
< 0.1%
ValueCountFrequency (%)
59.26637796 42
 
0.3%
59.17346796 76
0.5%
59.05128396 1
 
< 0.1%
58.99595196 118
0.8%
53.28437796 14
 
0.1%
47.80087836 8
 
0.1%
47.30687796 2
 
< 0.1%
47.30237796 10
 
0.1%
46.80687756 1
 
< 0.1%
46.80387756 6
 
< 0.1%

has_gas
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
False
11955 
True
2651 
ValueCountFrequency (%)
False 11955
81.8%
True 2651
 
18.2%
2023-08-14T14:38:54.517196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

imp_cons
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7752
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.7869
Minimum0
Maximum15042.79
Zeros6169
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:54.579368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.395
Q3193.98
95-th percentile638.8175
Maximum15042.79
Range15042.79
Interquartile range (IQR)193.98

Descriptive statistics

Standard deviation341.36937
Coefficient of variation (CV)2.2342843
Kurtosis380.8937
Mean152.7869
Median Absolute Deviation (MAD)37.395
Skewness13.198799
Sum2231605.4
Variance116533.04
MonotonicityNot monotonic
2023-08-14T14:38:54.652564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6169
42.2%
0.3 5
 
< 0.1%
0.1 5
 
< 0.1%
34.53 4
 
< 0.1%
26.26 4
 
< 0.1%
42.04 4
 
< 0.1%
126.6 4
 
< 0.1%
26.51 4
 
< 0.1%
117.18 4
 
< 0.1%
0.15 4
 
< 0.1%
Other values (7742) 8399
57.5%
ValueCountFrequency (%)
0 6169
42.2%
0.06 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 5
 
< 0.1%
0.14 1
 
< 0.1%
0.15 4
 
< 0.1%
0.17 2
 
< 0.1%
0.24 1
 
< 0.1%
0.27 1
 
< 0.1%
0.28 2
 
< 0.1%
ValueCountFrequency (%)
15042.79 1
< 0.1%
9682.89 1
< 0.1%
8732.6 1
< 0.1%
8254.16 1
< 0.1%
6787.12 1
< 0.1%
5836.49 1
< 0.1%
5343.76 1
< 0.1%
5311.97 1
< 0.1%
5019.25 1
< 0.1%
4925.36 1
< 0.1%

margin_gross_pow_ele
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2391
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.565121
Minimum0
Maximum374.64
Zeros157
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:54.733770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.56
Q114.28
median21.64
Q329.88
95-th percentile51.72
Maximum374.64
Range374.64
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation20.231172
Coefficient of variation (CV)0.82357305
Kurtosis35.892607
Mean24.565121
Median Absolute Deviation (MAD)8.12
Skewness4.4726321
Sum358798.16
Variance409.30031
MonotonicityNot monotonic
2023-08-14T14:38:54.817468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.04 258
 
1.8%
33.12 238
 
1.6%
29.76 170
 
1.2%
34.68 161
 
1.1%
0 157
 
1.1%
16.92 156
 
1.1%
23.76 156
 
1.1%
10.08 151
 
1.0%
19.2 141
 
1.0%
14.64 135
 
0.9%
Other values (2381) 12883
88.2%
ValueCountFrequency (%)
0 157
1.1%
0.03 1
 
< 0.1%
0.12 125
0.9%
0.24 16
 
0.1%
0.36 7
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.64 7
 
< 0.1%
0.66 1
 
< 0.1%
0.68 3
 
< 0.1%
ValueCountFrequency (%)
374.64 1
< 0.1%
314.76 1
< 0.1%
299.64 1
< 0.1%
248.64 1
< 0.1%
225.12 2
< 0.1%
224.89 1
< 0.1%
224.64 1
< 0.1%
219.88 1
< 0.1%
214.35 1
< 0.1%
214.14 1
< 0.1%

margin_net_pow_ele
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2391
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.562517
Minimum0
Maximum374.64
Zeros157
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:54.896780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.56
Q114.28
median21.64
Q329.88
95-th percentile51.72
Maximum374.64
Range374.64
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation20.23028
Coefficient of variation (CV)0.82362404
Kurtosis35.901232
Mean24.562517
Median Absolute Deviation (MAD)8.12
Skewness4.4733258
Sum358760.13
Variance409.26422
MonotonicityNot monotonic
2023-08-14T14:38:54.970969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.04 258
 
1.8%
33.12 238
 
1.6%
29.76 170
 
1.2%
34.68 161
 
1.1%
0 157
 
1.1%
16.92 156
 
1.1%
23.76 156
 
1.1%
10.08 151
 
1.0%
19.2 141
 
1.0%
14.64 135
 
0.9%
Other values (2381) 12883
88.2%
ValueCountFrequency (%)
0 157
1.1%
0.03 1
 
< 0.1%
0.12 125
0.9%
0.24 16
 
0.1%
0.36 7
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.64 7
 
< 0.1%
0.66 1
 
< 0.1%
0.68 3
 
< 0.1%
ValueCountFrequency (%)
374.64 1
< 0.1%
314.76 1
< 0.1%
299.64 1
< 0.1%
248.64 1
< 0.1%
225.12 2
< 0.1%
224.89 1
< 0.1%
224.64 1
< 0.1%
219.88 1
< 0.1%
214.35 1
< 0.1%
214.14 1
< 0.1%

nb_prod_act
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2923456
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:55.040545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum32
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70977352
Coefficient of variation (CV)0.54921339
Kurtosis258.95725
Mean1.2923456
Median Absolute Deviation (MAD)0
Skewness8.6368779
Sum18876
Variance0.50377845
MonotonicityNot monotonic
2023-08-14T14:38:55.088780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 11431
78.3%
2 2445
 
16.7%
3 523
 
3.6%
4 150
 
1.0%
5 31
 
0.2%
9 11
 
0.1%
6 8
 
0.1%
8 4
 
< 0.1%
10 2
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
1 11431
78.3%
2 2445
 
16.7%
3 523
 
3.6%
4 150
 
1.0%
5 31
 
0.2%
6 8
 
0.1%
8 4
 
< 0.1%
9 11
 
0.1%
10 2
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
32 1
 
< 0.1%
10 2
 
< 0.1%
9 11
 
0.1%
8 4
 
< 0.1%
6 8
 
0.1%
5 31
 
0.2%
4 150
 
1.0%
3 523
 
3.6%
2 2445
 
16.7%
1 11431
78.3%

net_margin
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct11965
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.26452
Minimum0
Maximum24570.65
Zeros185
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:55.157041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.16
Q150.7125
median112.53
Q3243.0975
95-th percentile587.685
Maximum24570.65
Range24570.65
Interquartile range (IQR)192.385

Descriptive statistics

Standard deviation311.79813
Coefficient of variation (CV)1.6474198
Kurtosis2642.9653
Mean189.26452
Median Absolute Deviation (MAD)75.32
Skewness36.569515
Sum2764397.6
Variance97218.074
MonotonicityNot monotonic
2023-08-14T14:38:55.232684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 185
 
1.3%
0.49 6
 
< 0.1%
35.98 5
 
< 0.1%
57.37 5
 
< 0.1%
86.52 5
 
< 0.1%
56.33 5
 
< 0.1%
0.01 5
 
< 0.1%
234.83 4
 
< 0.1%
78.22 4
 
< 0.1%
33.8 4
 
< 0.1%
Other values (11955) 14378
98.4%
ValueCountFrequency (%)
0 185
1.3%
0.01 5
 
< 0.1%
0.02 4
 
< 0.1%
0.03 3
 
< 0.1%
0.04 3
 
< 0.1%
0.05 3
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
24570.65 1
< 0.1%
10203.5 1
< 0.1%
4346.37 1
< 0.1%
4305.79 1
< 0.1%
3768.16 1
< 0.1%
3407.65 1
< 0.1%
3403.27 1
< 0.1%
3323.02 1
< 0.1%
3215.03 1
< 0.1%
2711.19 1
< 0.1%

num_years_antig
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9978091
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:55.296260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6117493
Coefficient of variation (CV)0.32249116
Kurtosis4.0781495
Mean4.9978091
Median Absolute Deviation (MAD)1
Skewness1.4462138
Sum72998
Variance2.5977357
MonotonicityNot monotonic
2023-08-14T14:38:55.354379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 4769
32.7%
4 3982
27.3%
3 2433
16.7%
5 2317
15.9%
7 509
 
3.5%
11 185
 
1.3%
12 110
 
0.8%
8 103
 
0.7%
9 92
 
0.6%
10 81
 
0.6%
Other values (3) 25
 
0.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 11
 
0.1%
3 2433
16.7%
4 3982
27.3%
5 2317
15.9%
6 4769
32.7%
7 509
 
3.5%
8 103
 
0.7%
9 92
 
0.6%
10 81
 
0.6%
ValueCountFrequency (%)
13 13
 
0.1%
12 110
 
0.8%
11 185
 
1.3%
10 81
 
0.6%
9 92
 
0.6%
8 103
 
0.7%
7 509
 
3.5%
6 4769
32.7%
5 2317
15.9%
4 3982
27.3%

origin_up
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
lxidpiddsbxsbosboudacockeimpuepw
7097 
kamkkxfxxuwbdslkwifmmcsiusiuosws
4294 
ldkssxwpmemidmecebumciepifcamkci
3148 
MISSING
 
64
usapbepcfoloekilkwsdiboslwaxobdp
 
2

Length

Max length32
Median length32
Mean length31.890456
Min length7

Characters and Unicode

Total characters465792
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowlxidpiddsbxsbosboudacockeimpuepw
2nd rowkamkkxfxxuwbdslkwifmmcsiusiuosws
3rd rowkamkkxfxxuwbdslkwifmmcsiusiuosws
4th rowkamkkxfxxuwbdslkwifmmcsiusiuosws
5th rowkamkkxfxxuwbdslkwifmmcsiusiuosws

Common Values

ValueCountFrequency (%)
lxidpiddsbxsbosboudacockeimpuepw 7097
48.6%
kamkkxfxxuwbdslkwifmmcsiusiuosws 4294
29.4%
ldkssxwpmemidmecebumciepifcamkci 3148
21.6%
MISSING 64
 
0.4%
usapbepcfoloekilkwsdiboslwaxobdp 2
 
< 0.1%
ewxeelcelemmiwuafmddpobolfuxioce 1
 
< 0.1%

Length

2023-08-14T14:38:55.412654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T14:38:55.483742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
lxidpiddsbxsbosboudacockeimpuepw 7097
48.6%
kamkkxfxxuwbdslkwifmmcsiusiuosws 4294
29.4%
ldkssxwpmemidmecebumciepifcamkci 3148
21.6%
missing 64
 
0.4%
usapbepcfoloekilkwsdiboslwaxobdp 2
 
< 0.1%
ewxeelcelemmiwuafmddpobolfuxioce 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 49063
10.5%
i 46771
10.0%
d 38984
 
8.4%
m 35722
 
7.7%
c 31084
 
6.7%
k 30573
 
6.6%
u 30228
 
6.5%
x 30228
 
6.5%
b 28740
 
6.2%
p 27594
 
5.9%
Other values (11) 116805
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 465344
99.9%
Uppercase Letter 448
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 49063
10.5%
i 46771
10.1%
d 38984
 
8.4%
m 35722
 
7.7%
c 31084
 
6.7%
k 30573
 
6.6%
u 30228
 
6.5%
x 30228
 
6.5%
b 28740
 
6.2%
p 27594
 
5.9%
Other values (6) 116357
25.0%
Uppercase Letter
ValueCountFrequency (%)
I 128
28.6%
S 128
28.6%
M 64
14.3%
N 64
14.3%
G 64
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 465792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 49063
10.5%
i 46771
10.0%
d 38984
 
8.4%
m 35722
 
7.7%
c 31084
 
6.7%
k 30573
 
6.6%
u 30228
 
6.5%
x 30228
 
6.5%
b 28740
 
6.2%
p 27594
 
5.9%
Other values (11) 116805
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 49063
10.5%
i 46771
10.0%
d 38984
 
8.4%
m 35722
 
7.7%
c 31084
 
6.7%
k 30573
 
6.6%
u 30228
 
6.5%
x 30228
 
6.5%
b 28740
 
6.2%
p 27594
 
5.9%
Other values (11) 116805
25.1%

pow_max
Real number (ℝ)

HIGH CORRELATION 

Distinct698
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.135136
Minimum3.3
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-08-14T14:38:55.566777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile10.39
Q112.5
median13.856
Q319.1725
95-th percentile41.5
Maximum320
Range316.7
Interquartile range (IQR)6.6725

Descriptive statistics

Standard deviation13.534743
Coefficient of variation (CV)0.74632711
Kurtosis59.202563
Mean18.135136
Median Absolute Deviation (MAD)3.056
Skewness5.7867849
Sum264881.79
Variance183.18928
MonotonicityNot monotonic
2023-08-14T14:38:55.638777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.2 2124
 
14.5%
10.392 2000
 
13.7%
13.856 1504
 
10.3%
15 583
 
4.0%
10.35 480
 
3.3%
19.8 416
 
2.8%
16.5 405
 
2.8%
20 294
 
2.0%
12.5 269
 
1.8%
13.15 234
 
1.6%
Other values (688) 6297
43.1%
ValueCountFrequency (%)
3.3 3
< 0.1%
3.464 1
 
< 0.1%
4 1
 
< 0.1%
5 2
< 0.1%
5.196 2
< 0.1%
5.75 2
< 0.1%
6 2
< 0.1%
6.9 1
 
< 0.1%
6.928 4
< 0.1%
7.7 1
 
< 0.1%
ValueCountFrequency (%)
320 1
 
< 0.1%
260 1
 
< 0.1%
200 4
< 0.1%
192 1
 
< 0.1%
180 2
< 0.1%
166 1
 
< 0.1%
164 1
 
< 0.1%
160 2
< 0.1%
155.88 1
 
< 0.1%
155 3
< 0.1%

churn
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
0
13187 
1
1419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14606
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Length

2023-08-14T14:38:55.712533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-14T14:38:55.776070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14606
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 14606
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Interactions

2023-08-14T14:38:49.131275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:27.797098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.139694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.553895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.851671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.089747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.614983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.862519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.159238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.447490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.997698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.280012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.557806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.824902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.394421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.622628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.867637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.205042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:27.872502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.211936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.625261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.922748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.163163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.687834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.939789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.230886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.527790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.068893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.353795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.632657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.895609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.467121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.693000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.936823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.274722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:27.974131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.285734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.699606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.989974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.262742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.754691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.012456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.304916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.602348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.137276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.422490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.704567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.966795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.538980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.763582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.004766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.352289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.060241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.386958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.776285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.067654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.347698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.829185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.090949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.384608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.682393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.216985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.500261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.783963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.045623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.615486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.839647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.085298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.421853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.129385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.462313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.847637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.133794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.424140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.895318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.166270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.457081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.756930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.284739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.568651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.854097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.118716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.683628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.907277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.153632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.496112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.211876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.533469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.924833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.206134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.502975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.971881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.244862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.530924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.836317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.360411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.651590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.933207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.194334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.758629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.991633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.225797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.568663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.292070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.602736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.001653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.278520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.575474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.042369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.321733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.607524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.913762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.434458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.730929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.004509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.269032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.835792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.068667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.299950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.642498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.369943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.673676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.077272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.355525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.663549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.113982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.395008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.680010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.221023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.510999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.803788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.078748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.341429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.906102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.139395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.374491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.718864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.452391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.751326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.153972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.426959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.741456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.189621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.471201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.755154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.297049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.583762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.877287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.152505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.417221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.972904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.210658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.445592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.798806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.538553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.830731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.236726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.507657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.826305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.269597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.553993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.832270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.380717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.666441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.954704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.229791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.497828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.050050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.288622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.525105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.877687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.616122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.901278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.313869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.582641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.908450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.342018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.631941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.907404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.460670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.751499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.027656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.303631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.571765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.122494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.358720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.620372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.959049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.689956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.973191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.390197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.654347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.984642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.416380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.710904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.983032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.537607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.826957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.101617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.381230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.653332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.193123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.430617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.693233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:50.047730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.772478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.043769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.468756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.727352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.058558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.493357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.788414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.060305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.617289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.901508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.176006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.454903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:44.732950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.267665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.504236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.770710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:50.132391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.849442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.120468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.549882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.802952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.324717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.569961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.863434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.140186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.696455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:40.981787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.250793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.534171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.097829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.339901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.576103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.845349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:50.215195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.921533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.349116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.625862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.873812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.393648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.642701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:36.941180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.223493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.773368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.057493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.323397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.606127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.172455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.409536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.649761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.918519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:50.295306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:28.993355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.414988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.701498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:32.944601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.469140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.719965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.011654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.298834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.846586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.128683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.394631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.677381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.245226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.478879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.722467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:48.990847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:50.377073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:29.067261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:30.484541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:31.774244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:33.014733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:34.544064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:35.790632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:37.084332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:38.372712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:39.923833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:41.202224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:42.465842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:43.752056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:45.320975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:46.553447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:47.799843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-14T14:38:49.060603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-08-14T14:38:55.837087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
cons_12mcons_gas_12mcons_last_monthforecast_cons_12mforecast_cons_yearforecast_discount_energyforecast_meter_rent_12mforecast_price_energy_off_peakforecast_price_energy_peakforecast_price_pow_off_peakimp_consmargin_gross_pow_elemargin_net_pow_elenb_prod_actnet_marginnum_years_antigpow_maxchannel_saleshas_gasorigin_upchurn
cons_12m1.0000.1610.7070.6950.429-0.0240.288-0.2860.323-0.3640.411-0.094-0.0940.1190.688-0.0100.3500.1400.2340.0660.049
cons_gas_12m0.1611.0000.1480.1370.0910.0050.058-0.0480.065-0.0750.093-0.026-0.0260.8650.134-0.0020.0760.0630.3020.0360.037
cons_last_month0.7070.1481.0000.4430.771-0.0180.351-0.2840.377-0.3700.759-0.002-0.0020.1090.4450.0090.3510.1170.2310.0460.041
forecast_cons_12m0.6950.1370.4431.0000.5080.0520.258-0.2490.267-0.3250.515-0.175-0.1750.1250.950-0.0600.3400.0000.0250.0540.019
forecast_cons_year0.4290.0910.7710.5081.0000.0140.426-0.3710.435-0.4440.987-0.011-0.0110.0720.516-0.0130.4250.0000.0000.0160.000
forecast_discount_energy-0.0240.005-0.0180.0520.0141.000-0.0030.2750.0900.1490.0330.2410.2400.1440.018-0.0830.0030.0250.0190.0330.023
forecast_meter_rent_12m0.2880.0580.3510.2580.426-0.0031.000-0.6080.690-0.6250.3970.1160.1160.0340.2930.0060.6600.0380.0580.0630.042
forecast_price_energy_off_peak-0.286-0.048-0.284-0.249-0.3710.275-0.6081.000-0.4700.714-0.3210.0410.0410.012-0.325-0.035-0.6700.0860.0680.0840.050
forecast_price_energy_peak0.3230.0650.3770.2670.4350.0900.690-0.4701.000-0.7310.4000.1760.1760.0650.326-0.0170.7060.0780.0610.0730.041
forecast_price_pow_off_peak-0.364-0.075-0.370-0.325-0.4440.149-0.6250.714-0.7311.000-0.392-0.139-0.140-0.030-0.4230.003-0.7150.0760.0690.0730.055
imp_cons0.4110.0930.7590.5150.9870.0330.397-0.3210.400-0.3921.000-0.017-0.0170.0770.501-0.0290.3870.0000.0210.0380.000
margin_gross_pow_ele-0.094-0.026-0.002-0.175-0.0110.2410.1160.0410.176-0.139-0.0171.0001.000-0.001-0.119-0.0410.2630.0220.0190.0400.089
margin_net_pow_ele-0.094-0.026-0.002-0.175-0.0110.2400.1160.0410.176-0.140-0.0171.0001.000-0.001-0.119-0.0410.2630.0220.0190.0400.089
nb_prod_act0.1190.8650.1090.1250.0720.1440.0340.0120.065-0.0300.077-0.001-0.0011.0000.108-0.0130.0480.0160.1150.0160.000
net_margin0.6880.1340.4450.9500.5160.0180.293-0.3250.326-0.4230.501-0.119-0.1190.1081.000-0.0560.4060.0000.0170.0120.027
num_years_antig-0.010-0.0020.009-0.060-0.013-0.0830.006-0.035-0.0170.003-0.029-0.041-0.041-0.013-0.0561.000-0.0260.2410.0430.2560.087
pow_max0.3500.0760.3510.3400.4250.0030.660-0.6700.706-0.7150.3870.2630.2630.0480.406-0.0261.0000.0060.0300.0390.026
channel_sales0.1400.0630.1170.0000.0000.0250.0380.0860.0780.0760.0000.0220.0220.0160.0000.2410.0061.0000.0530.2540.081
has_gas0.2340.3020.2310.0250.0000.0190.0580.0680.0610.0690.0210.0190.0190.1150.0170.0430.0300.0531.0000.0020.023
origin_up0.0660.0360.0460.0540.0160.0330.0630.0840.0730.0730.0380.0400.0400.0160.0120.2560.0390.2540.0021.0000.097
churn0.0490.0370.0410.0190.0000.0230.0420.0500.0410.0550.0000.0890.0890.0000.0270.0870.0260.0810.0230.0971.000

Missing values

2023-08-14T14:38:50.522243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-14T14:38:50.868799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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024011ae4ebbe3035111d65fa7c15bc57foosdfpfkusacimwkcsosbicdxkicaua05494602013-06-152016-06-152015-11-012015-06-230.0000.01.780.1144810.09814240.606701t0.0025.4425.442678.993lxidpiddsbxsbosboudacockeimpuepw43.6481
1d29c2c54acc38ff3c0614d0a653813ddMISSING4660002009-08-212016-08-302009-08-212015-08-31189.9500.016.270.1457110.00000044.311378f0.0016.3816.38118.896kamkkxfxxuwbdslkwifmmcsiusiuosws13.8000
2764c75f661154dac3a6c254cd082ea7dfoosdfpfkusacimwkcsosbicdxkicaua544002010-04-162016-04-162010-04-162015-04-1747.9600.038.720.1657940.08789944.311378f0.0028.6028.6016.606kamkkxfxxuwbdslkwifmmcsiusiuosws13.8560
3bba03439a292a1e166f80264c16191cblmkebamcaaclubfxadlmueccxoimlema1584002010-03-302016-03-302010-03-302015-03-31240.0400.019.830.1466940.00000044.311378f0.0030.2230.22125.466kamkkxfxxuwbdslkwifmmcsiusiuosws13.2000
4149d57cf92fc41cf94415803a877cb4bMISSING442505262010-01-132016-03-072010-01-132015-03-09445.755260.0131.730.1169000.10001540.606701f52.3244.9144.91147.986kamkkxfxxuwbdslkwifmmcsiusiuosws19.8000
51aa498825382410b098937d65c4ec26dusilxuppasemubllopkaafesmlibmsdf8302019982011-12-092016-12-092015-11-012015-12-10796.9419980.030.120.1647750.08613145.308378f181.2133.1233.121118.894lxidpiddsbxsbosboudacockeimpuepw13.2001
67ab4bf4878d8f7661dfc20e9b8e18011foosdfpfkusacimwkcsosbicdxkicaua45097002011-12-022016-12-022011-12-022015-12-038069.2800.00.000.1661780.08753844.311378f0.004.044.041346.634lxidpiddsbxsbosboudacockeimpuepw15.0001
701495c955be7ec5e7f3203406785aae0foosdfpfkusacimwkcsosbicdxkicaua29552012602010-04-212016-04-212010-04-212015-04-22864.737510.0144.490.1151740.09883740.606701f70.6353.9253.921100.096lxidpiddsbxsbosboudacockeimpuepw26.4000
8f53a254b1115634330c12c7fdbf7958ausilxuppasemubllopkaafesmlibmsdf2962002011-09-232016-09-232011-09-232015-09-25444.3800.015.850.1457110.00000044.311378f0.0012.8212.82142.594kamkkxfxxuwbdslkwifmmcsiusiuosws13.2000
910c1b2f97a2d2a6f10299dc213d1a370lmkebamcaaclubfxadlmueccxoimlema26064021882010-05-042016-05-042015-04-292015-05-052738.1021880.0130.430.1157610.09941940.606701f219.5933.4233.421329.606lxidpiddsbxsbosboudacockeimpuepw31.5000
idchannel_salescons_12mcons_gas_12mcons_last_monthdate_activdate_enddate_modif_proddate_renewalforecast_cons_12mforecast_cons_yearforecast_discount_energyforecast_meter_rent_12mforecast_price_energy_off_peakforecast_price_energy_peakforecast_price_pow_off_peakhas_gasimp_consmargin_gross_pow_elemargin_net_pow_elenb_prod_actnet_marginnum_years_antigorigin_uppow_maxchurn
14596c3f4f737d598a1b47a94440bb18c3c06lmkebamcaaclubfxadlmueccxoimlema1097002011-02-092016-02-092011-02-092015-02-11165.6000.016.040.1466940.00000044.311378f0.0026.0426.04117.385ldkssxwpmemidmecebumciepifcamkci10.3920
14597ae818f3cc00ef5845416699aacc4bd7eewpakwlliwisiwduibdlfmalxowmwpci831006852012-12-182016-12-182012-12-182015-12-21833.056850.0131.760.1152370.10012340.939027f67.0324.0224.021102.523kamkkxfxxuwbdslkwifmmcsiusiuosws23.1000
145981582ef35fbfa265e60bb3399bdebac87MISSING944104802009-10-082016-10-082015-05-242015-10-09983.974800.0132.110.1152370.10012340.939027f46.9820.0020.001113.176ldkssxwpmemidmecebumciepifcamkci15.0010
1459946362cb1ad2fcdad347a6fa1bc1e5d4bfoosdfpfkusacimwkcsosbicdxkicaua18163303602010-01-262017-01-262015-11-172016-01-272663.8200.016.350.1435750.00000044.311378t0.0031.2031.203254.816kamkkxfxxuwbdslkwifmmcsiusiuosws13.8560
14600c49217f16a06263e5381eaba94a67a8bfoosdfpfkusacimwkcsosbicdxkicaua871460113672013-02-082016-02-082013-02-082015-02-09712.337130.0145.820.1203720.10348740.606701f71.8166.0066.00187.143lxidpiddsbxsbosboudacockeimpuepw26.4000
1460118463073fb097fc0ac5d3e040f356987foosdfpfkusacimwkcsosbicdxkicaua322704794002012-05-242016-05-082015-05-082014-05-264648.0100.018.570.1383050.00000044.311378t0.0027.8827.882381.774lxidpiddsbxsbosboudacockeimpuepw15.0000
14602d0a6f71671571ed83b2645d23af6de00foosdfpfkusacimwkcsosbicdxkicaua722301812012-08-272016-08-272012-08-272015-08-28631.691810.0144.030.1001670.09189258.995952f15.940.000.00190.343lxidpiddsbxsbosboudacockeimpuepw6.0001
1460310e6828ddd62cbcf687cb74928c4c2d2foosdfpfkusacimwkcsosbicdxkicaua184401792012-02-082016-02-072012-02-082015-02-09190.391790.0129.600.1169000.10001540.606701f18.0539.8439.84120.384lxidpiddsbxsbosboudacockeimpuepw15.9351
146041cf20fd6206d7678d5bcafd28c53b4dbfoosdfpfkusacimwkcsosbicdxkicaua131002012-08-302016-08-302012-08-302015-08-3119.3400.07.180.1457110.00000044.311378f0.0013.0813.0810.963lxidpiddsbxsbosboudacockeimpuepw11.0000
14605563dde550fd624d7352f3de77c0cdfcdMISSING8730002009-12-182016-12-172009-12-182015-12-21762.4100.01.070.1670860.08845445.311378f0.0011.8411.84196.346ldkssxwpmemidmecebumciepifcamkci10.3920